Browsing by Author "Li, Teng"
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Item Open Access Efficient decentralized task allocation for UAV swarms in multi-target surveillance missions(IEEE, 2019-08-15) Li, Teng; Shin, Hyo-Sang; Tsourdos, AntoniosThis paper deals with the large-scale task allocation problem for Unmanned Aerial Vehicle (UAV) swarms in surveillance missions. The task allocation problem is proven to be NP-hard which means that finding the optimal solution requires exponential time. This paper presents a practically efficient decentralized task allocation algorithm for UAV swarms based on lazy sample greedy. The proposed algorithm can provide a solution with an expected optimality ratio of at least p for monotone submodular objective functions and of p(1−p) for non-monotone submodular objective functions. The individual computational complexity for each UAV is O(pr2), where p∈(0,0.5] is the sampling probability, r is the number of tasks. The performance of the proposed algorithm is testified through digital simulations of a multi-target surveillance mission. Simulation results indicate that the proposed algorithm achieves a comparable solution quality to state-of-the-art algorithms with dramatically less running time. Moreover, a trade-off between the solution quality and the running time is obtained by adjusting the sampling probabilityItem Open Access Greedy based proactive spectrum handoff scheme for cognitive radio systems(IEEE, 2019-11-21) Xu, Zhengjia; Ivan, Petrunin; Li, Teng; Tsourdos, AntoniosThe aeronautical spectrum becomes increasingly congested due to raising number of non-stationary users, such as unmanned aerial vehicles (UAVs). With the growing demand to spectrum capacity, cognitive radio technology is a promising solution to maximize the utilization of spectrum by enabling communication of secondary users (SUs) without interfering with primary users (PUs). In this paper we formulate and solve a multi-parametric objective function for proactive handoff scheme in multiple input multiple output (MIMO) system constrained by QoS requirements. To improve the efficiency of handoff scheme for multiple communicating UAVs the greedy strategy is adopted. An innovative aspect of our solution includes consideration of quality of service (QoS) components, e.g. opportunistic service time, channel quality, etc. Some of these components, for example collision probability and false alarm probability, affect QoS in a negative way and are considered as constraints. Simulation of handoff scheme has been performed to evaluate the performance of the proposed algorithm in selecting multiple channels when the spectrum environment changes. The performance of handoff scheme is compared with random selection method and is found outperforming the random selection method in terms of averaged utilization ratio. Analysis of results has shown that the spectrum utilization ratio can be doubled by considering wider bandwidth (more channels) and by making QoS requirements less strict. In both cases this leads to near-linear increase in time consumption for handoff scheme generation.Item Open Access A sample decreasing threshold greedy‑based algorithm for big data summarisation(Springer, 2021-02-09) Li, Teng; Shin, Hyo-Sang; Tsourdos, AntoniosAs the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an efficient algorithm for maximising general non-negative submodular objective functions subject to k-extendible system constraints. Leveraging a random sampling process and a decreasing threshold strategy, this work proposes an algorithm, named Sample Decreasing Threshold Greedy (SDTG). The proposed algorithm obtains an expected approximation guarantee of 11+k−ϵ for maximising monotone submodular functions and of k(1+k)2−ϵ in non-monotone cases with expected computational complexity of O(n(1+k)ϵlnrϵ). Here, r is the largest size of feasible solutions, and ϵ∈(0,11+k) is an adjustable designing parameter for the trade-off between the approximation ratio and the computational complexity. The performance of the proposed algorithm is validated and compared with that of benchmark algorithms through experiments with a movie recommendation system based on a real database.Item Open Access Threshold bundle-based task allocation for multiple aerial robots(Elsevier, 2020-04-14) Li, Teng; Shin, Hyo-Sang; Tsourdos, AntoniosThis paper focuses on the large-scale task allocation problem for multiple Unmanned Aerial Vehicles (UAVs). One of the great challenges with task allocation is the NP-hardness for both computation and communication. This paper proposes an efficient decentralised task allocation algorithm for multiple UAVs to handle the NP-hardness while providing an optimality bound of solution quality. The proposed algorithm can reduce computational and communicating complexity by introducing a decreasing threshold and building task bundles based on the sequential greedy algorithm. The performance of the proposed algorithm is examined through Monte-Carlo simulations of a multi-target surveillance mission. Simulation results demonstrate that the proposed algorithm achieves similar solution quality compared with benchmark task allocation algorithms but consumes much less running time and consensus steps.